Bayesian approach to inference and variable selection for misclassified and under-reported response models.

Response partial missingness is a problem in studies conducted in a variety of disciplines. We investigate the impact ignoring response partial missingness has on determining a subset of significant covariates in non-linear regression. In particular, we consider non-differential misclassification in logistic regression and non-differential under-reporting in Poisson regression. Differential misclassification and differential under-reporting are also addressed but in less detail. We then develop a Bayesian approach to select significant covariates while accounting for the partial missingness. Examples of response partial missingness in which the variable selection method is applied include determining the factors that contribute to whether or not an individual will stop smoking and how many days an individual is absent from work.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Doctoral

Grantor

Baylor University.

Year dc:date.issued

2009

Author dc:creator

• Powers, Stephanie L.

Advisor dc:contributor.advisor

• Stamey, James D.

• Bayesian statistical decision theory.
• Regression analysis.
• Missing observations (Statistics)
• Error analysis (Mathematics)
• Poisson distribution.

Statement dc:rights

• Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission.

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/2104/5355

OAI identifier oai:identifier

oai:baylor-ir.tdl.org:2104/5355